Global exam grading algorithm under fire for suspected bias
Global exam grading algorithm
under fire for suspected bias
Students and experts say the
formula the International Baccalaureate program used to generate grades may be
discriminatory.
By Avi Asher-Schapiro Tuesday,
21 July 2020 15:38 GMT
NEW YORK, July 21 (Thomson
Reuters Foundation) - When Colorado high school student Isabel Castaneda
checked her final grades for the International Baccalaureate program in July,
she was shocked.
Despite being one of the
top-ranking students in her public school, she failed a number of courses —
including high-level Spanish, her native language.
The International
Baccalaureate (IB) program - a global standard of educational testing that also
allows U.S. high-school students to obtain college credit - cancelled its exams
in May, due to the coronavirus pandemic.
Instead of sitting final
exams, which usually account for the majority of students' scores, students
were assigned their marks based on a mathematical "awarding model",
as described by the IB program.
"I come from a
low-income family - and my entire last two years were driven by the goal of
getting as many college credits as I could to save money on school,"
Castaneda said in a phone interview. "When I saw those scores, my heart
sank."
The COVID-19 pandemic
has disrupted exams all over the world, and educational
institutions have adapted in a range of ways, from moving tests online to
asking students to wear protective gear during testing.
Relying on an algorithm to
help determine results comes with its own specific risks, researchers warn.
Depending on the kinds of
data the model considers, and how it makes predictions, it has the potential to
reproduce - or even exacerbate - existing patterns of inequality for low-income
and minority students, they say.
About 160,000 students take
IB courses every year, including nearly 90,000 in the United States - and
almost 60% of public schools that offer IB in the U.S. are "Title I"
schools, with significant low-income student populations, according to the
program.
"The choice to use a
statistical model in place of a traditional examination warrants several
concerns," said Esther Rolf, a PhD candidate at the University of
California-Berkeley, who studies algorithmic fairness.
"Using historical
records ... often leads to bias against individuals from historical
underprivileged groups."
IB spokesman Dan Rene shared
with the Thomson Reuters Foundation an explanation of the model which relied on
three main components.
They were coursework,
predictions teachers made about how students would perform on the exam, and the
"school context", which included historical data on predicted
results, and performance on past coursework for each subject.
"This process was
subjected to rigorous testing by educational statistic specialists," the
spokesman said in an emailed statement.
IB also released a statistical May bulletin showing that average scores
in 2020 were in line with previous years, and said it had a process to
"review extraordinary cases".
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In previous years, students'
grades have been generated by combining final exams graded by IB and coursework
marked by their teachers - which the IB spot-checked, according to its website.
Teachers also make
predictions about their students' final grades, which students can use to
secure provisional college admissions before taking their final exams.
"[A] school's own record
was built into the model" by using "historical data to model
predicted grade accuracy, as well as the record of the school to do better or
worse on examinations compared with coursework," the IB's statement noted.
Although the IB insists its
model is not an algorithm, experts say it is.
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